e0842e71dc
* v0.5 (#9) * update idql configs * update awr configs * update dipo configs * update qsm configs * update dqm configs * update project version to 0.5.0
136 lines
4.1 KiB
Python
136 lines
4.1 KiB
Python
"""
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Actor and Critic models for model-free online RL with DIffusion POlicy (DIPO).
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"""
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import torch
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import logging
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import copy
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log = logging.getLogger(__name__)
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from model.diffusion.diffusion import DiffusionModel
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from model.diffusion.sampling import make_timesteps
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class DIPODiffusion(DiffusionModel):
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def __init__(
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self,
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actor,
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critic,
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use_ddim=False,
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# modifying denoising schedule
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min_sampling_denoising_std=0.1,
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**kwargs,
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):
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super().__init__(network=actor, use_ddim=use_ddim, **kwargs)
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assert not self.use_ddim, "DQL does not support DDIM"
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self.critic = critic.to(self.device)
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# target critic
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self.critic_target = copy.deepcopy(self.critic)
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# reassign actor
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self.actor = self.network
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# target actor
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self.actor_target = copy.deepcopy(self.actor)
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# Minimum std used in denoising process when sampling action - helps exploration
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self.min_sampling_denoising_std = min_sampling_denoising_std
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# ---------- RL training ----------#
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def loss_critic(self, obs, next_obs, actions, rewards, terminated, gamma):
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# get current Q-function
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current_q1, current_q2 = self.critic(obs, actions)
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with torch.no_grad():
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# get next Q-function
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next_actions = self.forward(
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cond=next_obs,
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deterministic=False,
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) # forward() has no gradient, which is desired here.
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next_q1, next_q2 = self.critic_target(next_obs, next_actions)
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next_q = torch.min(next_q1, next_q2)
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# terminal state mask
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mask = 1 - terminated
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# flatten
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rewards = rewards.view(-1)
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next_q = next_q.view(-1)
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mask = mask.view(-1)
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# target value
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target_q = rewards + gamma * next_q * mask
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# Update critic
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loss_critic = torch.mean((current_q1 - target_q) ** 2) + torch.mean(
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(current_q2 - target_q) ** 2
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)
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return loss_critic
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def update_target_critic(self, tau):
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for target_param, source_param in zip(
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self.critic_target.parameters(), self.critic.parameters()
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):
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target_param.data.copy_(
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target_param.data * (1.0 - tau) + source_param.data * tau
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)
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def update_target_actor(self, tau):
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for target_param, source_param in zip(
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self.actor_target.parameters(), self.actor.parameters()
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):
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target_param.data.copy_(
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target_param.data * (1.0 - tau) + source_param.data * tau
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)
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# ---------- Sampling ----------#``
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# override
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@torch.no_grad()
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def forward(
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self,
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cond,
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deterministic=False,
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):
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"""Use target actor"""
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device = self.betas.device
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B = len(cond["state"])
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# Loop
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x = torch.randn((B, self.horizon_steps, self.action_dim), device=device)
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t_all = list(reversed(range(self.denoising_steps)))
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for i, t in enumerate(t_all):
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t_b = make_timesteps(B, t, device)
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mean, logvar = self.p_mean_var(
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x=x,
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t=t_b,
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cond=cond,
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network_override=self.actor_target,
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)
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std = torch.exp(0.5 * logvar)
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# Determine the noise level
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if deterministic and t == 0:
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std = torch.zeros_like(std)
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elif deterministic: # For DDPM, sample with noise
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std = torch.clip(std, min=1e-3)
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else:
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std = torch.clip(std, min=self.min_sampling_denoising_std)
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noise = torch.randn_like(x).clamp_(
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-self.randn_clip_value, self.randn_clip_value
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)
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x = mean + std * noise
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# clamp action at final step
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if self.final_action_clip_value is not None and i == len(t_all) - 1:
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x = torch.clamp(
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x, -self.final_action_clip_value, self.final_action_clip_value
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)
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return x
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